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A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
Epstein–Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9120175/ https://www.ncbi.nlm.nih.gov/pubmed/35589792 http://dx.doi.org/10.1038/s41467-022-30459-5 |
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| author | Zheng, Xueyi Wang, Ruixuan Zhang, Xinke Sun, Yan Zhang, Haohuan Zhao, Zihan Zheng, Yuanhang Luo, Jing Zhang, Jiangyu Wu, Hongmei Huang, Dan Zhu, Wenbiao Chen, Jianning Cao, Qinghua Zeng, Hong Luo, Rongzhen Li, Peng Lan, Lilong Yun, Jingping Xie, Dan Zheng, Wei-Shi Luo, Junhang Cai, Muyan |
| author_facet | Zheng, Xueyi Wang, Ruixuan Zhang, Xinke Sun, Yan Zhang, Haohuan Zhao, Zihan Zheng, Yuanhang Luo, Jing Zhang, Jiangyu Wu, Hongmei Huang, Dan Zhu, Wenbiao Chen, Jianning Cao, Qinghua Zeng, Hong Luo, Rongzhen Li, Peng Lan, Lilong Yun, Jingping Xie, Dan Zheng, Wei-Shi Luo, Junhang Cai, Muyan |
| author_sort | Zheng, Xueyi |
| collection | PubMed |
| description | Epstein–Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an averaged area under the receiver operating curve (AUROC) of 0.969 from the internal cross validation, an AUROC of 0.941 on an external dataset from multiple institutes and an AUROC of 0.895 on The Cancer Genome Atlas dataset. The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist. This finding suggests that our EBVNet could provide an innovative approach for the identification of EBVaGC and may help effectively select patients with gastric cancer for immunotherapy. |
| format | Online Article Text |
| id | pubmed-9120175 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2022 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-91201752022-05-21 A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology Zheng, Xueyi Wang, Ruixuan Zhang, Xinke Sun, Yan Zhang, Haohuan Zhao, Zihan Zheng, Yuanhang Luo, Jing Zhang, Jiangyu Wu, Hongmei Huang, Dan Zhu, Wenbiao Chen, Jianning Cao, Qinghua Zeng, Hong Luo, Rongzhen Li, Peng Lan, Lilong Yun, Jingping Xie, Dan Zheng, Wei-Shi Luo, Junhang Cai, Muyan Nat Commun Article Epstein–Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an averaged area under the receiver operating curve (AUROC) of 0.969 from the internal cross validation, an AUROC of 0.941 on an external dataset from multiple institutes and an AUROC of 0.895 on The Cancer Genome Atlas dataset. The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist. This finding suggests that our EBVNet could provide an innovative approach for the identification of EBVaGC and may help effectively select patients with gastric cancer for immunotherapy. Nature Publishing Group UK 2022-05-19 /pmc/articles/PMC9120175/ /pubmed/35589792 http://dx.doi.org/10.1038/s41467-022-30459-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
| spellingShingle | Article Zheng, Xueyi Wang, Ruixuan Zhang, Xinke Sun, Yan Zhang, Haohuan Zhao, Zihan Zheng, Yuanhang Luo, Jing Zhang, Jiangyu Wu, Hongmei Huang, Dan Zhu, Wenbiao Chen, Jianning Cao, Qinghua Zeng, Hong Luo, Rongzhen Li, Peng Lan, Lilong Yun, Jingping Xie, Dan Zheng, Wei-Shi Luo, Junhang Cai, Muyan A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology |
| title | A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology |
| title_full | A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology |
| title_fullStr | A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology |
| title_full_unstemmed | A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology |
| title_short | A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology |
| title_sort | deep learning model and human-machine fusion for prediction of ebv-associated gastric cancer from histopathology |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9120175/ https://www.ncbi.nlm.nih.gov/pubmed/35589792 http://dx.doi.org/10.1038/s41467-022-30459-5 |
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